add rel

data/icews14/relations.dict

@@ -12407,3 +12407,233 @@
 12406	Carry out roadside bombing[65]
 12407	Appeal for target to allow international involvement (non-mediation)[1]
 12408	Reject request for change in leadership[179]
+12409	Criticize or denounce
+12410	Express intent to meet or negotiate
+12411	Consult
+12412	Make an appeal or request
+12413	Abduct, hijack, or take hostage
+12414	Praise or endorse
+12415	Engage in negotiation
+12416	Use unconventional violence
+12417	Make statement
+12418	Arrest, detain, or charge with legal action
+12419	Use conventional military force
+12420	Complain officially
+12421	Impose administrative sanctions
+12422	Express intent to cooperate
+12423	Make a visit
+12424	Appeal for de-escalation of military engagement
+12425	Sign formal agreement
+12426	Attempt to assassinate
+12427	Host a visit
+12428	Increase military alert status
+12429	Impose embargo, boycott, or sanctions
+12430	Provide economic aid
+12431	Demonstrate or rally
+12432	Express intent to engage in diplomatic cooperation (such as policy support)
+12433	Appeal for intelligence
+12434	Demand
+12435	Carry out suicide bombing
+12436	Threaten
+12437	Express intent to provide material aid
+12438	Grant diplomatic recognition
+12439	Meet at a 'third' location
+12440	Accuse
+12441	Investigate
+12442	Reject
+12443	Appeal for diplomatic cooperation (such as policy support)
+12444	Engage in symbolic act
+12445	Defy norms, law
+12446	Consider policy option
+12447	Provide aid
+12448	Sexually assault
+12449	Make empathetic comment
+12450	Bring lawsuit against
+12451	Impose blockade, restrict movement
+12452	Make pessimistic comment
+12453	Protest violently, riot
+12454	Reduce or break diplomatic relations
+12455	Grant asylum
+12456	Engage in diplomatic cooperation
+12457	Make optimistic comment
+12458	Torture
+12459	Refuse to yield
+12460	Appeal for change in leadership
+12461	Cooperate militarily
+12462	Mobilize or increase armed forces
+12463	fight with small arms and light weapons
+12464	Ease administrative sanctions
+12465	Appeal for political reform
+12466	Return, release person(s)
+12467	Discuss by telephone
+12468	Demonstrate for leadership change
+12469	Impose restrictions on political freedoms
+12470	Reduce relations
+12471	Investigate crime, corruption
+12472	Engage in material cooperation
+12473	Appeal to others to meet or negotiate
+12474	Provide humanitarian aid
+12475	Use tactics of violent repression
+12476	Occupy territory
+12477	Demand humanitarian aid
+12478	Threaten non-force
+12479	Express intent to cooperate economically
+12480	Conduct suicide, car, or other non-military bombing
+12481	Demand diplomatic cooperation (such as policy support)
+12482	Demand meeting, negotiation
+12483	Deny responsibility
+12484	Express intent to change institutions, regime
+12485	Give ultimatum
+12486	Appeal for judicial cooperation
+12487	Rally support on behalf of
+12488	Obstruct passage, block
+12489	Share intelligence or information
+12490	Expel or deport individuals
+12491	Confiscate property
+12492	Accuse of aggression
+12493	Physically assault
+12494	Retreat or surrender militarily
+12495	Veto
+12496	Kill by physical assault
+12497	Assassinate
+12498	Appeal for change in institutions, regime
+12499	Forgive
+12500	Reject proposal to meet, discuss, or negotiate
+12501	Express intent to provide humanitarian aid
+12502	Appeal for release of persons or property
+12503	Acknowledge or claim responsibility
+12504	Ease economic sanctions, boycott, embargo
+12505	Express intent to cooperate militarily
+12506	Cooperate economically
+12507	Express intent to provide economic aid
+12508	Mobilize or increase police power
+12509	Employ aerial weapons
+12510	Accuse of human rights abuses
+12511	Conduct strike or boycott
+12512	Appeal for policy change
+12513	Demonstrate military or police power
+12514	Provide military aid
+12515	Reject plan, agreement to settle dispute
+12516	Yield
+12517	Appeal for easing of administrative sanctions
+12518	Mediate
+12519	Apologize
+12520	Express intent to release persons or property
+12521	Express intent to de-escalate military engagement
+12522	Accede to demands for rights
+12523	Demand economic aid
+12524	Impose state of emergency or martial law
+12525	Receive deployment of peacekeepers
+12526	Demand de-escalation of military engagement
+12527	Declare truce, ceasefire
+12528	Reduce or stop humanitarian assistance
+12529	Appeal to others to settle dispute
+12530	Reject request for military aid
+12531	Threaten with political dissent, protest
+12532	Appeal to engage in or accept mediation
+12533	Express intent to ease economic sanctions, boycott, or embargo
+12534	Coerce
+12535	fight with artillery and tanks
+12536	Express intent to cooperate on intelligence
+12537	Express intent to settle dispute
+12538	Express accord
+12539	Decline comment
+12540	Rally opposition against
+12541	Halt negotiations
+12542	Demand that target yields
+12543	Appeal for military aid
+12544	Threaten with military force
+12545	Express intent to provide military protection or peacekeeping
+12546	Threaten with sanctions, boycott, embargo
+12547	Express intent to provide military aid
+12548	Demand change in leadership
+12549	Appeal for economic aid
+12550	Refuse to de-escalate military engagement
+12551	Refuse to release persons or property
+12552	Increase police alert status
+12553	Return, release property
+12554	Ease military blockade
+12555	Appeal for material cooperation
+12556	Express intent to cooperate on judicial matters
+12557	Appeal for economic cooperation
+12558	Demand settling of dispute
+12559	Accuse of crime, corruption
+12560	Defend verbally
+12561	Provide military protection or peacekeeping
+12562	Accuse of espionage, treason
+12563	Seize or damage property
+12564	Accede to requests or demands for political reform
+12565	Appeal for easing of economic sanctions, boycott, or embargo
+12566	Threaten to reduce or stop aid
+12567	Engage in judicial cooperation
+12568	Appeal to yield
+12569	Demand military aid
+12570	Refuse to ease administrative sanctions
+12571	Demand release of persons or property
+12572	Accede to demands for change in leadership
+12573	Appeal for humanitarian aid
+12574	Threaten with repression
+12575	Demand change in institutions, regime
+12576	Demonstrate for policy change
+12577	Appeal for aid
+12578	Appeal for rights
+12579	Engage in violent protest for rights
+12580	Express intent to mediate
+12581	Expel or withdraw peacekeepers
+12582	Appeal for military protection or peacekeeping
+12583	Engage in mass killings
+12584	Accuse of war crimes
+12585	Reject military cooperation
+12586	Threaten to halt negotiations
+12587	Ban political parties or politicians
+12588	Express intent to change leadership
+12589	Demand material cooperation
+12590	Express intent to institute political reform
+12591	Demand easing of administrative sanctions
+12592	Express intent to engage in material cooperation
+12593	Reduce or stop economic assistance
+12594	Express intent to ease administrative sanctions
+12595	Demand intelligence cooperation
+12596	Ease curfew
+12597	Receive inspectors
+12598	Demand rights
+12599	Demand political reform
+12600	Demand judicial cooperation
+12601	Engage in political dissent
+12602	Detonate nuclear weapons
+12603	Violate ceasefire
+12604	Express intent to accept mediation
+12605	Refuse to ease economic sanctions, boycott, or embargo
+12606	Demand mediation
+12607	Obstruct passage to demand leadership change
+12608	Express intent to yield
+12609	Conduct hunger strike
+12610	Threaten to halt mediation
+12611	Reject judicial cooperation
+12612	Reduce or stop military assistance
+12613	Ease political dissent
+12614	Threaten to reduce or break relations
+12615	Demobilize armed forces
+12616	Use as human shield
+12617	Demand policy change
+12618	Accede to demands for change in institutions, regime
+12619	Reject economic cooperation
+12620	Reject material cooperation
+12621	Halt mediation
+12622	Accede to demands for change in policy
+12623	Investigate war crimes
+12624	Threaten with administrative sanctions
+12625	Reduce or stop material aid
+12626	Destroy property
+12627	Express intent to change policy
+12628	Use chemical, biological, or radiological weapons
+12629	Reject request for military protection or peacekeeping
+12630	Demand material aid
+12631	Engage in mass expulsion
+12632	Investigate human rights abuses
+12633	Carry out car bombing
+12634	Expel or withdraw
+12635	Ease state of emergency or martial law
+12636	Carry out roadside bombing
+12637	Appeal for target to allow international involvement (non-mediation)
+12638	Reject request for change in leadership

data/wikidata12k_both/relations.dict

@@ -421,3 +421,27 @@
 420	P551[36-69]
 421	P579[0-15]
 422	P102[54-62]
+423	P131
+424	P1435
+425	P39
+426	P54
+427	P31
+428	P463
+429	P512
+430	P190
+431	P150
+432	P1376
+433	P166
+434	P2962
+435	P108
+436	P17
+437	P793
+438	P69
+439	P26
+440	P579
+441	P1411
+442	P6
+443	P1346
+444	P102
+445	P27
+446	P551

main.py

@@ -91,9 +91,11 @@ class Main(object):
         for line in open('./data/{}/{}'.format(self.p.dataset, "relations.dict")):
             id, rel = map(str.lower, line.strip().split('\t'))
             self.rel2id[rel] = int(id)
+            rel_set.add(rel)
 
         # self.ent2id = {ent: idx for idx, ent in enumerate(ent_set)}
         # self.rel2id = {rel: idx for idx, rel in enumerate(rel_set)}
+        
         self.rel2id.update({rel+'_reverse': idx+len(self.rel2id)
                            for idx, rel in enumerate(rel_set)})
 
@@ -111,47 +113,48 @@ class Main(object):
         for split in ['train', 'test', 'valid']:
             for line in open('./data/{}/{}.txt'.format(self.p.dataset, split)):
                 sub, rel, obj, *_ = map(str.lower, line.replace('\xa0', '').strip().split('\t'))
-                sub, rel, obj = self.ent2id[sub], self.rel2id[rel], self.ent2id[obj]
+                nt_rel = rel.split('[')[0]
-                self.data[split].append((sub, rel, obj))
+                sub, rel, obj, nt_rel = self.ent2id[sub], self.rel2id[rel], self.ent2id[obj], self.rel2id[nt_rel]
+                self.data[split].append((sub, rel, obj, nt_rel))
 
                 if split == 'train':
-                    sr2o[(sub, rel)].add(obj)
+                    sr2o[(sub, rel, nt_rel)].add(obj)
-                    sr2o[(obj, rel+self.p.num_rel)].add(sub)
+                    sr2o[(obj, rel+self.p.num_rel, nt_rel + self.p.num_rel)].add(sub)
         self.data = dict(self.data)
 
         self.sr2o = {k: list(v) for k, v in sr2o.items()}
         for split in ['test', 'valid']:
-            for sub, rel, obj in self.data[split]:
+            for sub, rel, obj, nt_rel in self.data[split]:
-                sr2o[(sub, rel)].add(obj)
+                sr2o[(sub, rel, nt_rel)].add(obj)
-                sr2o[(obj, rel+self.p.num_rel)].add(sub)
+                sr2o[(obj, rel+self.p.num_rel, nt_rel + self.p.num_rel)].add(sub)
 
         self.sr2o_all = {k: list(v) for k, v in sr2o.items()}
 
         self.triples = ddict(list)
 
         if self.p.train_strategy == 'one_to_n':
-            for (sub, rel), obj in self.sr2o.items():
+            for (sub, rel, nt_rel), obj in self.sr2o.items():
                 self.triples['train'].append(
-                    {'triple': (sub, rel, -1), 'label': self.sr2o[(sub, rel)], 'sub_samp': 1})
+                    {'triple': (sub, rel, -1, nt_rel), 'label': self.sr2o[(sub, rel, nt_rel)], 'sub_samp': 1})
         else:
-            for sub, rel, obj in self.data['train']:
+            for sub, rel, obj, nt_rel in self.data['train']:
                 rel_inv = rel + self.p.num_rel
-                sub_samp = len(self.sr2o[(sub, rel)]) + \
+                sub_samp = len(self.sr2o[(sub, rel, nt_rel)]) + \
-                    len(self.sr2o[(obj, rel_inv)])
+                    len(self.sr2o[(obj, rel_inv, nt_rel + self.p.num_rel)])
                 sub_samp = np.sqrt(1/sub_samp)
 
                 self.triples['train'].append({'triple': (
-                    sub, rel, obj),     'label': self.sr2o[(sub, rel)],     'sub_samp': sub_samp})
+                    sub, rel, obj, nt_rel),     'label': self.sr2o[(sub, rel, nt_rel)],     'sub_samp': sub_samp})
                 self.triples['train'].append({'triple': (
-                    obj, rel_inv, sub), 'label': self.sr2o[(obj, rel_inv)], 'sub_samp': sub_samp})
+                    obj, rel_inv, sub, nt_rel + self.p.num_rel), 'label': self.sr2o[(obj, rel_inv, nt_rel + self.p.num_rel)], 'sub_samp': sub_samp})
 
         for split in ['test', 'valid']:
-            for sub, rel, obj in self.data[split]:
+            for sub, rel, obj, nt_rel in self.data[split]:
                 rel_inv = rel + self.p.num_rel
                 self.triples['{}_{}'.format(split, 'tail')].append(
-                    {'triple': (sub, rel, obj), 	   'label': self.sr2o_all[(sub, rel)]})
+                    {'triple': (sub, rel, obj, nt_rel), 	   'label': self.sr2o_all[(sub, rel, nt_rel)]})
                 self.triples['{}_{}'.format(split, 'head')].append(
-                    {'triple': (obj, rel_inv, sub), 'label': self.sr2o_all[(obj, rel_inv)]})
+                    {'triple': (obj, rel_inv, sub, nt_rel + self.p.num_rel), 'label': self.sr2o_all[(obj, rel_inv, nt_rel + self.p.num_rel)]})
 
         self.triples = dict(self.triples)
 
@@ -275,13 +278,13 @@ class Main(object):
             if self.p.train_strategy == 'one_to_x':
                 triple, label, neg_ent, sub_samp = [
                     _.to(self.device) for _ in batch]
-                return triple[:, 0], triple[:, 1], triple[:, 2], label, neg_ent, sub_samp
+                return triple[:, 0], triple[:, 1], triple[:, 2], triple[:, 3], label, neg_ent, sub_samp
             else:
                 triple, label = [_.to(self.device) for _ in batch]
-                return triple[:, 0], triple[:, 1], triple[:, 2], label, None, None
+                return triple[:, 0], triple[:, 1], triple[:, 2], triple[:, 3], label, None, None
         else:
             triple, label = [_.to(self.device) for _ in batch]
-            return triple[:, 0], triple[:, 1], triple[:, 2], label
+            return triple[:, 0], triple[:, 1], triple[:, 2], triple[:, 3], label
 
     def save_model(self, save_path):
         """
@@ -416,8 +419,8 @@ class Main(object):
             obj_pred = []
             obj_pred_score = []
             for step, batch in enumerate(train_iter):
-                sub, rel, obj, label = self.read_batch(batch, split)
+                sub, rel, obj, nt_rel, label = self.read_batch(batch, split)
-                pred = self.model.forward(sub, rel, None, 'one_to_n')
+                pred = self.model.forward(sub, rel, nt_rel, None, 'one_to_n')
                 b_range = torch.arange(pred.size()[0], device=self.device)
                 target_pred = pred[b_range, obj]
                 pred = torch.where(label.byte(), torch.zeros_like(pred), pred)
@@ -474,10 +477,10 @@ class Main(object):
         for step, batch in enumerate(train_iter):
             self.optimizer.zero_grad()
 
-            sub, rel, obj, label, neg_ent, sub_samp = self.read_batch(
+            sub, rel, obj, nt_rel, label, neg_ent, sub_samp = self.read_batch(
                 batch, 'train')
 
-            pred = self.model.forward(sub, rel, neg_ent, self.p.train_strategy)
+            pred = self.model.forward(sub, rel, nt_rel, neg_ent, self.p.train_strategy)
             loss = self.model.loss(pred, label, sub_samp)
 
             loss.backward()
@@ -690,7 +693,7 @@ if __name__ == "__main__":
                 collate_fn=TrainDataset.collate_fn
             ))
         for step, batch in enumerate(dataloader):
-            sub, rel, obj, label, neg_ent, sub_samp = model.read_batch(
+            sub, rel, obj, nt_rel, label, neg_ent, sub_samp = model.read_batch(
                 batch, 'train')
             
             if (neg_ent is None):

models.py

@@ -10,8 +10,6 @@ from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
 from timm.models.layers import DropPath, trunc_normal_
 from timm.models.registry import register_model
 from timm.layers.helpers import to_2tuple
-from typing import *
-import math
 
 
 class ConvE(torch.nn.Module):
@@ -468,6 +466,10 @@ class FouriER(torch.nn.Module):
             self.p.ent_vec_dim, image_h*image_w)
         torch.nn.init.xavier_normal_(self.ent_fusion.weight)
 
+        self.ent_attn = torch.nn.Linear(
+            128, 128)
+        torch.nn.init.xavier_normal_(self.ent_attn.weight)
+
         self.rel_fusion = torch.nn.Linear(
             self.p.rel_vec_dim, image_h*image_w)
         torch.nn.init.xavier_normal_(self.rel_fusion.weight)
@@ -528,22 +530,6 @@ class FouriER(torch.nn.Module):
 
         self.network = nn.ModuleList(network)
         self.norm = norm_layer(embed_dims[-1])
-        self.graph_type = 'Spatial'
-        N = (image_h // patch_size)**2
-        if self.graph_type in ["Spatial", "Mixed"]:
-            # Create a range tensor of node indices
-            indices = torch.arange(N)
-            # Reshape the indices tensor to create a grid of row and column indices
-            row_indices = indices.view(-1, 1).expand(-1, N)
-            col_indices = indices.view(1, -1).expand(N, -1)
-            # Compute the adjacency matrix
-            row1, col1 = row_indices // int(math.sqrt(N)), row_indices % int(math.sqrt(N))
-            row2, col2 = col_indices // int(math.sqrt(N)), col_indices % int(math.sqrt(N))
-            graph = ((abs(row1 - row2) <= 1).float() * (abs(col1 - col2) <= 1).float())
-            graph = graph - torch.eye(N)
-            self.spatial_graph = graph.cuda() # comment .to("cuda") if the environment is cpu
-        self.class_token = False
-        self.token_scale = False
         self.head = nn.Linear(
                 embed_dims[-1], num_classes) if num_classes > 0 \
                 else nn.Identity()
@@ -561,49 +547,19 @@ class FouriER(torch.nn.Module):
 
     def forward_tokens(self, x):
         outs = []
-        B, C, H, W = x.shape
-        N = H*W
-        if self.graph_type in ["Semantic", "Mixed"]:
-            # Generate the semantic graph w.r.t. the cosine similarity between tokens
-            # Compute cosine similarity
-            if self.class_token:
-                x_normed = x[:, 1:] / x[:, 1:].norm(dim=-1, keepdim=True)
-            else:
-                x_normed = x / x.norm(dim=-1, keepdim=True)
-            x_cossim = x_normed @ x_normed.transpose(-1, -2)
-            threshold = torch.kthvalue(x_cossim, N-1-self.num_neighbours, dim=-1, keepdim=True)[0] # B,H,1,1 
-            semantic_graph = torch.where(x_cossim>=threshold, 1.0, 0.0)
-            if self.class_token:
-                semantic_graph = semantic_graph - torch.eye(N-1, device=semantic_graph.device).unsqueeze(0)
-            else:
-                semantic_graph = semantic_graph - torch.eye(N, device=semantic_graph.device).unsqueeze(0)
-        
-        if self.graph_type == "None":
-            graph = None
-        else:
-            if self.graph_type == "Spatial":
-                graph = self.spatial_graph.unsqueeze(0).expand(B,-1,-1)#.to(x.device)
-            elif self.graph_type == "Semantic":
-                graph = semantic_graph
-            elif self.graph_type == "Mixed":
-                # Integrate the spatial graph and semantic graph
-                spatial_graph = self.spatial_graph.unsqueeze(0).expand(B,-1,-1).to(x.device)
-                graph = torch.bitwise_or(semantic_graph.int(), spatial_graph.int()).float()
-            
-            # Symmetrically normalize the graph
-            degree = graph.sum(-1) # B, N
-            degree = torch.diag_embed(degree**(-1/2))
-            graph = degree @ graph @ degree
-
         for idx, block in enumerate(self.network):
-            try:
+            x = block(x)
-                x = block(x, graph)
-            except:
-                x = block(x)
         # output only the features of last layer for image classification
         return x
+    
+    def fuse_attention(self, s_embedding, l_embedding):
+        w1 = self.ent_attn(torch.tanh(s_embedding))
+        w2 = self.ent_attn(torch.tanh(l_embedding))
+        aff = F.softmax(torch.cat((w1,w2),1), 1)
+        en_embedding = aff[:,0].unsqueeze(1) * s_embedding + aff[:, 1].unsqueeze(1) * l_embedding
+        return en_embedding
 
-    def forward(self, sub, rel, neg_ents, strategy='one_to_x'):
+    def forward(self, sub, rel, nt_rel, neg_ents, strategy='one_to_x'):
         sub_emb = self.ent_fusion(self.ent_embed(sub))
         rel_emb = self.rel_fusion(self.rel_embed(rel))
         comb_emb = torch.stack([sub_emb.view(-1, self.p.image_h, self.p.image_w), rel_emb.view(-1, self.p.image_h, self.p.image_w)], dim=1)
@@ -612,6 +568,17 @@ class FouriER(torch.nn.Module):
         z = self.forward_embeddings(y)
         z = self.forward_tokens(z)
         z = z.mean([-2, -1])
+
+        nt_rel_emb = self.rel_fusion(self.rel_embed(nt_rel))
+        comb_emb_1 = torch.stack([sub_emb.view(-1, self.p.image_h, self.p.image_w), nt_rel_emb.view(-1, self.p.image_h, self.p.image_w)], dim=1)
+        y_1 = comb_emb_1.view(-1, 2, self.p.image_h, self.p.image_w)
+        y_1 = self.bn0(y_1)
+        z_1 = self.forward_embeddings(y_1)
+        z_1 = self.forward_tokens(z_1)
+        z_1 = z_1.mean([-2, -1])
+
+        z = self.fuse_attention(z, z_1)
+
         z = self.norm(z)
         x = self.head(z)
         x = self.hidden_drop(x)
@@ -758,7 +725,7 @@ def basic_blocks(dim, index, layers,
             use_layer_scale=use_layer_scale, 
             layer_scale_init_value=layer_scale_init_value, 
             ))
-    blocks = SeqModel(*blocks)
+    blocks = nn.Sequential(*blocks)
 
     return blocks
 
@@ -923,279 +890,6 @@ def window_reverse(windows, window_size, H, W):
     x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, -1, H, W)
     return x
 
-class SeqModel(nn.Sequential):
-	def forward(self, *inputs):
-		for module in self._modules.values():
-			if type(inputs) == tuple:
-				inputs = module(*inputs)
-			else:
-				inputs = module(inputs)
-		return inputs
-
-def propagate(x: torch.Tensor, weight: torch.Tensor, 
-              index_kept: torch.Tensor, index_prop: torch.Tensor, 
-              standard: str = "None", alpha: Optional[float] = 0, 
-              token_scales: Optional[torch.Tensor] = None,
-              cls_token=True):
-    """
-    Propagate tokens based on the selection results.
-    ================================================
-    Args:
-        - x: Tensor([B, N, C]): the feature map of N tokens, including the [CLS] token.
-        
-        - weight: Tensor([B, N-1, N-1]): the weight of each token propagated to the other tokens, 
-                                         excluding the [CLS] token. weight could be a pre-defined 
-                                         graph of the current feature map (by default) or the
-                                         attention map (need to manually modify the Block Module).
-                                         
-        - index_kept: Tensor([B, N-1-num_prop]): the index of kept image tokens in the feature map X
-        
-        - index_prop: Tensor([B, num_prop]): the index of propagated image tokens in the feature map X
-        
-        - standard: str: the method applied to propagate the tokens, including "None", "Mean" and 
-                         "GraphProp"
-        
-        - alpha: float: the coefficient of propagated features
-        
-        - token_scales: Tensor([B, N]): the scale of tokens, including the [CLS] token. token_scales
-                                        is None by default. If it is not None, then token_scales 
-                                        represents the scales of each token and should sum up to N.
-        
-    Return:
-        - x: Tensor([B, N-1-num_prop, C]): the feature map after propagation
-        
-        - weight: Tensor([B, N-1-num_prop, N-1-num_prop]): the graph of feature map after propagation
-        
-        - token_scales: Tensor([B, N-1-num_prop]): the scale of tokens after propagation
-    """
-    
-    B, N, C = x.shape
-    
-    # Step 1: divide tokens
-    if cls_token:
-        x_cls = x[:, 0:1] # B, 1, C
-    x_kept = x.gather(dim=1, index=index_kept.unsqueeze(-1).expand(-1,-1,C)) # B, N-1-num_prop, C
-    x_prop = x.gather(dim=1, index=index_prop.unsqueeze(-1).expand(-1,-1,C)) # B, num_prop, C
-    
-    # Step 2: divide token_scales if it is not None
-    if token_scales is not None:
-        if cls_token:
-            token_scales_cls = token_scales[:, 0:1] # B, 1
-        token_scales_kept = token_scales.gather(dim=1, index=index_kept) # B, N-1-num_prop
-        token_scales_prop = token_scales.gather(dim=1, index=index_prop) # B, num_prop
-    
-    # Step 3: propagate tokens
-    if standard == "None":
-        """
-        No further propagation
-        """
-        pass
-        
-    elif standard == "Mean":
-        """
-        Calculate the mean of all the propagated tokens,
-        and concatenate the result token back to kept tokens.
-        """
-        # naive average
-        x_prop = x_prop.mean(1, keepdim=True) # B, 1, C
-        # Concatenate the average token 
-        x_kept = torch.cat((x_kept, x_prop), dim=1) # B, N-num_prop, C
-            
-    elif standard == "GraphProp":
-        """
-        Propagate all the propagated token to kept token
-        with respect to the weights and token scales.
-        """
-        assert weight is not None, "The graph weight is needed for graph propagation"
-        
-        # Step 3.1: divide propagation weights.
-        if cls_token:
-            index_kept = index_kept - 1 # since weights do not include the [CLS] token
-            index_prop = index_prop - 1 # since weights do not include the [CLS] token
-            weight = weight.gather(dim=1, index=index_kept.unsqueeze(-1).expand(-1,-1,N-1)) # B, N-1-num_prop, N-1
-            weight_prop = weight.gather(dim=2, index=index_prop.unsqueeze(1).expand(-1,weight.shape[1],-1)) # B, N-1-num_prop, num_prop
-            weight = weight.gather(dim=2, index=index_kept.unsqueeze(1).expand(-1,weight.shape[1],-1)) # B, N-1-num_prop, N-1-num_prop
-        else:
-            weight = weight.gather(dim=1, index=index_kept.unsqueeze(-1).expand(-1,-1,N)) # B, N-1-num_prop, N-1
-            weight_prop = weight.gather(dim=2, index=index_prop.unsqueeze(1).expand(-1,weight.shape[1],-1)) # B, N-1-num_prop, num_prop
-            weight = weight.gather(dim=2, index=index_kept.unsqueeze(1).expand(-1,weight.shape[1],-1)) # B, N-1-num_prop, N-1-num_prop
-        
-        # Step 3.2: generate the broadcast message and propagate the message to corresponding kept tokens
-        # Simple implementation
-        x_prop = weight_prop @ x_prop # B, N-1-num_prop, C
-        x_kept = x_kept + alpha * x_prop # B, N-1-num_prop, C
-        
-        """ scatter_reduce implementation for batched inputs
-        # Get the non-zero values
-        non_zero_indices = torch.nonzero(weight_prop, as_tuple=True)
-        non_zero_values = weight_prop[non_zero_indices]
-        
-        # Sparse multiplication
-        batch_indices, row_indices, col_indices = non_zero_indices
-        sparse_matmul = alpha * non_zero_values[:, None] * x_prop[batch_indices, col_indices, :]
-        reduce_indices = batch_indices * x_kept.shape[1] + row_indices
-        
-        x_kept = x_kept.reshape(-1, C).scatter_reduce(dim=0, 
-                                                      index=reduce_indices[:, None], 
-                                                      src=sparse_matmul, 
-                                                      reduce="sum",
-                                                      include_self=True)
-        x_kept = x_kept.reshape(B, -1, C)
-        """
-        
-        # Step 3.3: calculate the scale of each token if token_scales is not None
-        if token_scales is not None:
-            if cls_token:
-                token_scales_cls = token_scales[:, 0:1] # B, 1
-                token_scales = token_scales[:, 1:]
-            token_scales_kept = token_scales.gather(dim=1, index=index_kept) # B, N-1-num_prop
-            token_scales_prop = token_scales.gather(dim=1, index=index_prop) # B, num_prop
-            token_scales_prop = weight_prop @ token_scales_prop.unsqueeze(-1) # B, N-1-num_prop, 1
-            token_scales = token_scales_kept + alpha * token_scales_prop.squeeze(-1) # B, N-1-num_prop
-            if cls_token:
-                token_scales = torch.cat((token_scales_cls, token_scales), dim=1) # B, N-num_prop
-    else:
-        assert False, "Propagation method \'%f\' has not been supported yet." % standard
-    
-    
-    if cls_token:
-        # Step 4： concatenate the [CLS] token and generate returned value
-        x = torch.cat((x_cls, x_kept), dim=1) # B, N-num_prop, C
-    else:
-        x = x_kept
-    return x, weight, token_scales
-
-
-
-def select(weight: torch.Tensor, standard: str = "None", num_prop: int = 0, cls_token = True):
-    """
-    Select image tokens to be propagated. The [CLS] token will be ignored. 
-    ======================================================================
-    Args:
-        - weight: Tensor([B, H, N, N]): used for selecting the kept tokens. Only support the
-                                        attention map of tokens at the moment.
-        
-        - standard: str: the method applied to select the tokens
-        
-        - num_prop: int: the number of tokens to be propagated
-        
-    Return:
-        - index_kept: Tensor([B, N-1-num_prop]): the index of kept tokens 
-        
-        - index_prop: Tensor([B, num_prop]): the index of propagated tokens
-    """
-    
-    assert len(weight.shape) == 4, "Selection methods on tensors other than the attention map haven't been supported yet."
-    B, H, N1, N2 = weight.shape
-    assert N1 == N2, "Selection methods on tensors other than the attention map haven't been supported yet."
-    N = N1
-    assert num_prop >= 0, "The number of propagated/pruned tokens must be non-negative."
-    
-    if cls_token:
-        if standard == "CLSAttnMean":
-            token_rank = weight[:,:,0,1:].mean(1)
-            
-        elif standard == "CLSAttnMax":
-            token_rank = weight[:,:,0,1:].max(1)[0]
-                
-        elif standard == "IMGAttnMean":
-            token_rank = weight[:,:,:,1:].sum(-2).mean(1)
-        
-        elif standard == "IMGAttnMax":
-            token_rank = weight[:,:,:,1:].sum(-2).max(1)[0]
-                
-        elif standard == "DiagAttnMean":
-            token_rank = torch.diagonal(weight, dim1=-2, dim2=-1)[:,:,1:].mean(1)
-            
-        elif standard == "DiagAttnMax":
-            token_rank = torch.diagonal(weight, dim1=-2, dim2=-1)[:,:,1:].max(1)[0]
-            
-        elif standard == "MixedAttnMean":
-            token_rank_1 = torch.diagonal(weight, dim1=-2, dim2=-1)[:,:,1:].mean(1)
-            token_rank_2 = weight[:,:,:,1:].sum(-2).mean(1)
-            token_rank = token_rank_1 * token_rank_2
-            
-        elif standard == "MixedAttnMax":
-            token_rank_1 = torch.diagonal(weight, dim1=-2, dim2=-1)[:,:,1:].max(1)[0]
-            token_rank_2 = weight[:,:,:,1:].sum(-2).max(1)[0]
-            token_rank = token_rank_1 * token_rank_2
-            
-        elif standard == "SumAttnMax":
-            token_rank_1 = torch.diagonal(weight, dim1=-2, dim2=-1)[:,:,1:].max(1)[0]
-            token_rank_2 = weight[:,:,:,1:].sum(-2).max(1)[0]
-            token_rank = token_rank_1 + token_rank_2
-            
-        elif standard == "CosSimMean":
-            weight = weight[:,:,1:,:].mean(1)
-            weight = weight / weight.norm(dim=-1, keepdim=True)
-            token_rank = -(weight @ weight.transpose(-1, -2)).sum(-1)
-        
-        elif standard == "CosSimMax":
-            weight = weight[:,:,1:,:].max(1)[0]
-            weight = weight / weight.norm(dim=-1, keepdim=True)
-            token_rank = -(weight @ weight.transpose(-1, -2)).sum(-1)
-            
-        elif standard == "Random":
-            token_rank = torch.randn((B, N-1), device=weight.device)
-                
-        else:
-            print("Type\'", standard, "\' selection not supported.")
-            assert False
-        
-        token_rank = torch.argsort(token_rank, dim=1, descending=True) # B, N-1
-        index_kept = token_rank[:, :-num_prop]+1 # B, N-1-num_prop
-        index_prop = token_rank[:, -num_prop:]+1 # B, num_prop
-            
-    else:
-        if standard == "IMGAttnMean":
-            token_rank = weight.sum(-2).mean(1)
-        
-        elif standard == "IMGAttnMax":
-            token_rank = weight.sum(-2).max(1)[0]
-                
-        elif standard == "DiagAttnMean":
-            token_rank = torch.diagonal(weight, dim1=-2, dim2=-1).mean(1)
-            
-        elif standard == "DiagAttnMax":
-            token_rank = torch.diagonal(weight, dim1=-2, dim2=-1).max(1)[0]
-            
-        elif standard == "MixedAttnMean":
-            token_rank_1 = torch.diagonal(weight, dim1=-2, dim2=-1).mean(1)
-            token_rank_2 = weight.sum(-2).mean(1)
-            token_rank = token_rank_1 * token_rank_2
-            
-        elif standard == "MixedAttnMax":
-            token_rank_1 = torch.diagonal(weight, dim1=-2, dim2=-1).max(1)[0]
-            token_rank_2 = weight.sum(-2).max(1)[0]
-            token_rank = token_rank_1 * token_rank_2
-            
-        elif standard == "SumAttnMax":
-            token_rank_1 = torch.diagonal(weight, dim1=-2, dim2=-1).max(1)[0]
-            token_rank_2 = weight.sum(-2).max(1)[0]
-            token_rank = token_rank_1 + token_rank_2
-            
-        elif standard == "CosSimMean":
-            weight = weight.mean(1)
-            weight = weight / weight.norm(dim=-1, keepdim=True)
-            token_rank = -(weight @ weight.transpose(-1, -2)).sum(-1)
-        
-        elif standard == "CosSimMax":
-            weight = weight.max(1)[0]
-            weight = weight / weight.norm(dim=-1, keepdim=True)
-            token_rank = -(weight @ weight.transpose(-1, -2)).sum(-1)
-            
-        elif standard == "Random":
-            token_rank = torch.randn((B, N-1), device=weight.device)
-                
-        else:
-            print("Type\'", standard, "\' selection not supported.")
-            assert False
-        
-        token_rank = torch.argsort(token_rank, dim=1, descending=True) # B, N-1
-        index_kept = token_rank[:, :-num_prop] # B, N-1-num_prop
-        index_prop = token_rank[:, -num_prop:] # B, num_prop
-    return index_kept, index_prop
-
 class PoolFormerBlock(nn.Module):
     """
     Implementation of one PoolFormer block.
@@ -1238,20 +932,13 @@ class PoolFormerBlock(nn.Module):
             self.layer_scale_2 = nn.Parameter(
                 layer_scale_init_value * torch.ones((dim)), requires_grad=True)
 
-    def forward(self, x, weight, token_scales = None):
+    def forward(self, x):
         B, C, H, W = x.shape
         x_windows = window_partition(x, self.window_size)
         x_windows = x_windows.view(-1, self.window_size * self.window_size, C)
         attn_windows = self.token_mixer(x_windows, mask=self.attn_mask)
         attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
         x_attn = window_reverse(attn_windows, self.window_size, H, W)
-        index_kept, index_prop = select(x_attn, standard="MixedAttnMax", num_prop=0,
-                                            cls_token=False)
-        original_shape = x_attn.shape
-        x_attn = x_attn.view(-1, self.window_size * self.window_size, C)
-        x_attn, weight, token_scales = propagate(x_attn, weight, index_kept, index_prop, standard="GraphProp",
-                                        alpha=0.1, token_scales=token_scales, cls_token=False)
-        x_attn = x_attn.view(*original_shape)
         if self.use_layer_scale:
             x = x + self.drop_path(
                 self.layer_scale_1.unsqueeze(-1).unsqueeze(-1)